AMD MI300X GPU Performance Analysis
By: Chandrish Ambati, Trung Diep
Potential Business Impact:
Makes AI models run much faster on new chips.
The rapid growth of large language models (LLMs) has driven the need for high-performance, scalable GPU hardware capable of efficiently serving models with hundreds of billions of parameters. While NVIDIA GPUs have traditionally dominated LLM deployments due to their mature CUDA software stack and state-of the-art accelerators, AMD's latest MI300X GPUs offer a compelling alternative, featuring high HBM capacity, matrix cores, and their proprietary interconnect. In this paper, we present a comprehensive evaluation of the AMD MI300X GPUs across key performance domains critical to LLM inference including compute throughput, memory bandwidth, and interconnect communication.
Similar Papers
Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design
Computation and Language
New computers learn faster using AMD chips.
Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design
Computation and Language
Makes big computer brains train faster on new chips.
Inter-APU Communication on AMD MI300A Systems via Infinity Fabric: a Deep Dive
Distributed, Parallel, and Cluster Computing
Makes supercomputers share data faster between parts.